A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
scIGANs: single-cell RNA-seq imputation using generative adversarial networks
2020
Nucleic Acids Research
Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, termed dropouts. Computational approaches have been proposed to recover the biologically meaningful expression by borrowing information from similar cells in the observed dataset. However, these methods
doi:10.1093/nar/gkaa506
pmid:32588900
pmcid:PMC7470961
fatcat:inqnoipnfnfrfoyuswk5oycvei